Build AI That Moves Into Production
Rackner is seeking an AI/ML Engineer to help shape how artificial intelligence is evaluated, built, governed, and deployed across a federal data modernization environment.
This role goes beyond isolated prototypes or model development. You will help determine where AI can create meaningful operational value, whether AI is the right solution, and how promising use cases move from concept into production.
You will have the opportunity to build AI applications, agents, intelligent workflows, and automation while also influencing how emerging AI and LLM technologies are evaluated, governed, monitored, and adopted. Your work may span use-case assessment, solution design, prototyping, testing, deployment, responsible-AI controls, and post-production monitoring.
Working alongside engineering, data, cybersecurity, governance, and mission teams, you will gain visibility across the full AI delivery lifecycle and help shape both the technology and the decisions behind it.
For an engineer who wants to expand beyond a narrow model-development role, this position offers exposure across:
Applied AI engineering → AI/LLM evaluation → intelligent automation → responsible AI → enterprise adoption
You will help turn emerging AI capabilities into practical tools that support real operational needs—not innovation for innovation’s sake.
What You'll Own
Turn operational challenges into practical AI use cases, technical requirements, and solution designs
Build and deploy AI applications, agents, intelligent workflows, and process automations
Take solutions from intake and prototype through testing, production deployment, and monitoring
Apply Python to AI/ML prototypes, integrations, evaluations, and automation
Connect AI capabilities with enterprise applications, APIs, workflows, and data platforms
Create intelligent document workflows for classification, extraction, summarization, routing, and related use cases
Evaluate AI, generative AI, and LLM platforms for quality, security, reliability, scalability, risk, and organizational fit
Design pilots, evaluation criteria, test plans, success measures, and adoption recommendations
Shape responsible-AI controls covering model evaluation, performance monitoring, human oversight, risk, and incident response
Apply tools such as Power Automate, Power Apps, Copilot Studio, or comparable intelligent-automation platforms
Develop AI-driven analytics capabilities including predictive modeling, forecasting, and natural-language interaction with enterprise data
Partner with stakeholders and technical teams to move useful AI capabilities from concept into sustainable operational use
What You'll Bring
A track record of building, integrating, evaluating, or deploying AI/ML capabilities in real organizational environments
Strong Python skills
Hands-on work with generative AI, LLM applications, machine learning, intelligent automation, or related applied-AI technologies
Ability to integrate solutions with APIs, enterprise systems, workflows, or data sources
Understanding of the AI delivery lifecycle from requirements and prototyping through testing, deployment, monitoring, and iteration
Working knowledge of responsible AI, governance, model evaluation, or AI risk-management practices
Technical judgment to assess feasibility, value, implementation complexity, and risk
Ability to translate operational needs into practical technical solutions
Clear communication across both technical and nontechnical stakeholders
Active Secret clearance
Valuable Additional Background
You do not need every item below to be successful in the role.
Background in one or more of these areas would be valuable:
Generative AI and enterprise LLM applications
AI agents or agentic workflows
Power Automate, Power Apps, Copilot Studio, or similar automation platforms
Azure AI, Microsoft Fabric, or related Microsoft technologies
Intelligent document processing
Predictive analytics, forecasting, or natural-language analytics
AI governance, human-in-the-loop controls, model monitoring, or evaluation frameworks
Organizational AI pilots or technology-selection efforts
Responsible AI within DoD or other regulated environments
Federal, defense, or education technology environments
Why Rackner
At Rackner, you will work on technology intended for real mission use—not innovation theater.
This role offers the opportunity to help determine where AI creates value, how solutions should be built, and what responsible production adoption looks like within a complex federal environment.
You will collaborate with teams working across AI/ML, cloud, data, DevSecOps, cybersecurity, and modern software engineering while g
